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https://github.com/PaddlePaddle/FastDeploy.git
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fad82b4e5d
* Add PaddleOCR Support * Add PaddleOCR Support * Add PaddleOCRv3 Support * Add PaddleOCRv3 Support * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleOCRv3 Support * Add PaddleOCRv3 Supports * Add PaddleOCRv3 Suport * Fix Rec diff * Remove useless functions * Remove useless comments * Add PaddleOCRv2 Support * Add PaddleOCRv3 & PaddleOCRv2 Support * remove useless parameters * Add utils of sorting det boxes * Fix code naming convention * Fix code naming convention * Fix code naming convention * Fix bug in the Classify process * Imporve OCR Readme * Fix diff in Cls model * Update Model Download Link in Readme
146 lines
4.0 KiB
Python
146 lines
4.0 KiB
Python
import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--det_model", required=True, help="Path of Detection model of PPOCR.")
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parser.add_argument(
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"--cls_model",
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required=True,
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help="Path of Classification model of PPOCR.")
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parser.add_argument(
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"--rec_model",
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required=True,
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help="Path of Recognization model of PPOCR.")
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parser.add_argument(
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"--rec_label_file",
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required=True,
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help="Path of Recognization model of PPOCR.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--det_use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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parser.add_argument(
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"--cls_use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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parser.add_argument(
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"--rec_use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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return parser.parse_args()
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def build_det_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.det_use_trt:
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option.use_trt_backend()
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#det_max_side_len 默认为960,当用户更改DET模型的max_side_len参数时，请将此参数同时更改
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det_max_side_len = 960
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option.set_trt_input_shape("x", [1, 3, 50, 50], [1, 3, 640, 640],
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[1, 3, det_max_side_len, det_max_side_len])
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return option
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def build_cls_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.cls_use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("x", [1, 3, 32, 100])
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return option
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def build_rec_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.rec_use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
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[1, 3, 48, 2000])
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return option
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args = parse_arguments()
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#Det模型
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det_model_file = os.path.join(args.det_model, "inference.pdmodel")
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det_params_file = os.path.join(args.det_model, "inference.pdiparams")
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#Cls模型
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cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
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cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
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#Rec模型
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rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
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rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
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rec_label_file = args.rec_label_file
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#默认
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det_model = fd.vision.ocr.DBDetector()
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cls_model = fd.vision.ocr.Classifier()
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rec_model = fd.vision.ocr.Recognizer()
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#模型初始化
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if (len(args.det_model) != 0):
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det_runtime_option = build_det_option(args)
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det_model = fd.vision.ocr.DBDetector(
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det_model_file, det_params_file, runtime_option=det_runtime_option)
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if (len(args.cls_model) != 0):
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cls_runtime_option = build_cls_option(args)
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file, cls_params_file, runtime_option=cls_runtime_option)
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if (len(args.rec_model) != 0):
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rec_runtime_option = build_rec_option(args)
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file,
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rec_params_file,
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rec_label_file,
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runtime_option=rec_runtime_option)
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ppocrsysv3 = fd.vision.ocr.PPOCRSystemv3(
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ocr_det=det_model._model,
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ocr_cls=cls_model._model,
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ocr_rec=rec_model._model)
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# 预测图片准备
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im = cv2.imread(args.image)
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#预测并打印结果
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result = ppocrsysv3.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_ppocr(im, result)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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